import os
import time
import torch
import mat4py
import argparse
import copy
import numpy as np
import pandas as pd
import seaborn as sns
from easydict import EasyDict
import torch.nn.functional as F
import matplotlib.pyplot as plt
from IPython.display import display, Image

from package.optim.ft_net import FTNetTrainer, parse_args
from package.optim.eanet_trainer import EANetTrainer
from package.eval.np_distance import compute_dist, compute_dist_with_visibility
from package.utils.file import walkdir
from package.utils.rank_list import get_rank_list, save_rank_list_to_im
from package.data.datasets.market1501 import Market1501

from package.utils.image import read_im, save_im, make_im_grid


from os.path import join
import shutil
from sklearn.model_selection import train_test_split
import pickle

def normalize(nparray, order=2, axis=0):
    """Normalize a N-D numpy array along the specified axis."""
    norm = np.linalg.norm(nparray, ord=order, axis=axis, keepdims=True)
    return nparray / (norm + np.finfo(np.float32).eps)

# def get_pap_mask(im_path):
#     return market1501_loader.dataset.get_pap_mask('/'.join(im_path.split('/')[2:]))

def get_img_list(path):
    is_image_file = lambda x : any(x.endswith(extension) 
                                   for extension in ['.png', '.jpg', '.jpeg', '.PNG', '.JPG', '.JPEG'])
    return [os.path.join(path, x) for x in os.listdir(path) if is_image_file(x)]

def get_file_name(im_path):
    return np.array(map(lambda x:x.split('/')[-1].split('.')[0], im_path))

def add_border(im, border_width, value):
    assert (im.ndim == 3) and (im.shape[0] == 3)
    im = np.copy(im)

    if isinstance(value, np.ndarray):
        # reshape to [3, 1, 1]
        value = value.flatten()[:, np.newaxis, np.newaxis]
    im[:, :border_width, :] = value
    im[:, -border_width:, :] = value
    im[:, :, :border_width] = value
    im[:, :, -border_width:] = value

    return im

def save_rank_list_to_im(rank_list, q_im_path, save_path, same_id=None, resize_h_w=(128, 64)):
    ims = [read_im(q_im_path, convert_rgb=True, resize_h_w=resize_h_w, transpose=True)]
    for i, ind in enumerate(rank_list):
        im = read_im(ind, convert_rgb=True, resize_h_w=resize_h_w, transpose=True)
        if same_id is not None:
            # Add green boundary to true positive, red to false positive
            color = np.array([0, 255, 0]) if same_id[i] else np.array([255, 0, 0])
            im = add_border(im, 3, color)
        ims.append(im)
    im = make_im_grid(ims, 1, len(rank_list) + 1, 8, 255)
    save_path = os.path.join(save_path, q_im_path.split('/')[-1] )
    save_im(im, save_path, transpose=True)
    
def get_file_name(path):
    return np.array(map(lambda x:x.split('/')[-1].split('.')[0], path))

def file_name(file_dir):   
    L=[]   
    for root, dirs, files in os.walk(file_dir):  
        for file in files:  
            L.append(os.path.join(root, file))  
    return L  

def get_rank_list(dist_vec, label, pic_num = 7):
    rank_list = []
    same_id = []
    i = 0
    sort_inds = np.argsort(dist_vec)
    
    for index, ind in  zip(sort_inds, g_im_paths):
        same_id.append( get_file_name(index)[:-1] == get_file_name(label)[:-1] )
        rank_list.append(ind)
        i += 1
        if i >= pic_num:
            break
    return rank_list, same_id

def get_img_name(paths):
    return np.array(map(lambda x:int(x.split('/')[-1].split('.')[0]), paths))

def get_att_prob(im_paths, attr_list):
        index = cfg.attr_index
        tmp = map(lambda x: F.softmax(x, dim=1).cpu().numpy().tolist(), attr_list, )
        return pd.DataFrame(tmp, columns=im_paths, index=index)
def get_att_label(im_paths, att):
    index = cfg.attr_index
    # tmp = map(lambda x: x.cpu().numpy(), att)
    return pd.DataFrame(att, columns=im_paths, index=index)
def get_choice(q_mat, att):
    # 返回DataFrame,横坐标图片路径,纵坐标属性,[序号]
    func = lambda x: x.index(max(x))
    return q_mat.applymap(func)
def get_score(q_mat, att):
    # 返回DataFrame,横坐标图片路径,纵坐标属性,[概率]
    func = lambda x: '%.2f' % max(x)
    return q_mat.applymap(func)
def get_gt(a, b):
    t = copy.deepcopy(a)
    for j in a.columns:
        t[j] = a[j] == b[j]
    return t

zhuoshi_imgs

# 模型文件
path = './backup/coop_1/ckpt.pth'

#　测试图片存放路径
test_img_zhuoshi_dir = 'test_distance/zhuoshi/resize/'
coop_dataset_path = '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/Total.txt'
test_img_coop_imgs = '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/train.pkl'
root = '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop'

attr_index = ['年龄类型', '背包类型', '上衣颜色', '上衣类型', '发型', '帽子类型', '种族类型', '性别', '裤子颜色', '裤子类型']
anno = {
    '年龄类型': ['未成年','青年','中年','老年','未知'],
    '背包类型': ['未背包','单肩包','双肩包','手提包','未知'],
    '上衣颜色' :['白','黑','红','黄','蓝','绿','紫','棕','灰','橙','未知',],
    '上衣类型' :['长袖','短袖','未知',],
    '发型'    : ['长发','短发','未知',],
    '帽子类型' :['未戴帽','鸭舌帽','其他帽','未知',],
    '种族类型' :['黄种人','白人','黑人','未知',],
    '性别':   [ '男','女','未知',],
    '裤子颜色': ['白','黑','红','黄','蓝','绿','紫','棕','灰','橙','未知',],
    '裤子类型': ['长裤','短裤','未知'],
}

# gallery图片列表
zhuoshi_im_paths= get_img_list(test_img_zhuoshi_dir)

读取测试集, 从中随机选出1000张图片Â¶

paths = []
attrs = []

with open(test_img_coop_imgs, 'r') as f:
    tmp = pickle.load(f)
    for i in tmp:
        attrs.append(i['att'])
        paths.append(os.path.join(root, i['im_path']))

label = pd.DataFrame(attrs, index=paths, columns=attr_index).sample(n=2000).sort_index(axis=0)
label

加载模型Â¶

ftnet = FTNetTrainer()
ftnet.init_cfg(args=parse_args())
pth_param = torch.load(path)
ftnet.model.load_state_dict(pth_param['state_dicts']['model'])
ftnet.model.eval()

ow_file is: paper_configs/PCB.txt
ow_str is: cfg.dataset.train.name='Coop'; 
=> Experiment Output Directory: exp/eanet/test_paper_models/PCB/market1501
[PYTORCH VERSION]: 1.0.1.post2
Loaded pickle file dataset/Coop/train.pkl
=========================
Coop
=========================
   split: train
# images: 187691
=========================

Keys not found in source state_dict: 
	 num_batches_tracked  x53
Keys not found in destination state_dict: 
	 fc.weight
	 fc.bias
=> Loaded ImageNet Model: imagenet_model/resnet50-19c8e357.pth
Model Structure:
Model(
  (backbone): ResNet(
    (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (relu): ReLU(inplace)
    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
    (layer1): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (downsample): Sequential(
          (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (2): Bottleneck(
        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
    )
    (layer2): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (downsample): Sequential(
          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (2): Bottleneck(
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (3): Bottleneck(
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
    )
    (layer3): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (downsample): Sequential(
          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (2): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (3): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (4): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (5): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
    )
    (layer4): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (downsample): Sequential(
          (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (2): Bottleneck(
        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
    )
  )
  (em_list): ModuleList(
    (0): Sequential(
      (0): Linear(in_features=2048, out_features=256, bias=True)
      (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
    )
    (1): Sequential(
      (0): Linear(in_features=2048, out_features=256, bias=True)
      (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
    )
    (2): Sequential(
      (0): Linear(in_features=2048, out_features=256, bias=True)
      (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
    )
    (3): Sequential(
      (0): Linear(in_features=2048, out_features=256, bias=True)
      (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
    )
    (4): Sequential(
      (0): Linear(in_features=2048, out_features=256, bias=True)
      (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
    )
    (5): Sequential(
      (0): Linear(in_features=2048, out_features=256, bias=True)
      (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
    )
  )
  (att_fc_list): ModuleList(
    (0): Sequential(
      (0): Linear(in_features=1536, out_features=1536, bias=True)
      (1): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): Linear(in_features=1536, out_features=1536, bias=True)
      (4): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (5): Linear(in_features=1536, out_features=5, bias=True)
    )
    (1): Sequential(
      (0): Linear(in_features=1536, out_features=1536, bias=True)
      (1): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): Linear(in_features=1536, out_features=1536, bias=True)
      (4): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (5): Linear(in_features=1536, out_features=5, bias=True)
    )
    (2): Sequential(
      (0): Linear(in_features=1536, out_features=1536, bias=True)
      (1): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): Linear(in_features=1536, out_features=1536, bias=True)
      (4): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (5): Linear(in_features=1536, out_features=11, bias=True)
    )
    (3): Sequential(
      (0): Linear(in_features=1536, out_features=1536, bias=True)
      (1): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): Linear(in_features=1536, out_features=1536, bias=True)
      (4): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (5): Linear(in_features=1536, out_features=3, bias=True)
    )
    (4): Sequential(
      (0): Linear(in_features=1536, out_features=1536, bias=True)
      (1): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): Linear(in_features=1536, out_features=1536, bias=True)
      (4): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (5): Linear(in_features=1536, out_features=3, bias=True)
    )
    (5): Sequential(
      (0): Linear(in_features=1536, out_features=1536, bias=True)
      (1): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): Linear(in_features=1536, out_features=1536, bias=True)
      (4): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (5): Linear(in_features=1536, out_features=4, bias=True)
    )
    (6): Sequential(
      (0): Linear(in_features=1536, out_features=1536, bias=True)
      (1): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): Linear(in_features=1536, out_features=1536, bias=True)
      (4): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (5): Linear(in_features=1536, out_features=4, bias=True)
    )
    (7): Sequential(
      (0): Linear(in_features=1536, out_features=1536, bias=True)
      (1): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): Linear(in_features=1536, out_features=1536, bias=True)
      (4): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (5): Linear(in_features=1536, out_features=3, bias=True)
    )
    (8): Sequential(
      (0): Linear(in_features=1536, out_features=1536, bias=True)
      (1): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): Linear(in_features=1536, out_features=1536, bias=True)
      (4): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (5): Linear(in_features=1536, out_features=11, bias=True)
    )
    (9): Sequential(
      (0): Linear(in_features=1536, out_features=1536, bias=True)
      (1): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): Linear(in_features=1536, out_features=1536, bias=True)
      (4): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (5): Linear(in_features=1536, out_features=3, bias=True)
    )
  )
)
Loaded pickle file dataset/Coop/test.pkl
=========================
Coop
=========================
   split: test
# images: 20856
=========================

ow_file is: paper_configs/PCB.txt
ow_str is: cfg.dataset.train.name='Coop';

Model(
  (backbone): ResNet(
    (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (relu): ReLU(inplace)
    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
    (layer1): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (downsample): Sequential(
          (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (2): Bottleneck(
        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
    )
    (layer2): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (downsample): Sequential(
          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (2): Bottleneck(
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (3): Bottleneck(
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
    )
    (layer3): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (downsample): Sequential(
          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (2): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (3): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (4): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (5): Bottleneck(
        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
    )
    (layer4): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (downsample): Sequential(
          (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): Bottleneck(
        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
      (2): Bottleneck(
        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
      )
    )
  )
  (em_list): ModuleList(
    (0): Sequential(
      (0): Linear(in_features=2048, out_features=256, bias=True)
      (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
    )
    (1): Sequential(
      (0): Linear(in_features=2048, out_features=256, bias=True)
      (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
    )
    (2): Sequential(
      (0): Linear(in_features=2048, out_features=256, bias=True)
      (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
    )
    (3): Sequential(
      (0): Linear(in_features=2048, out_features=256, bias=True)
      (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
    )
    (4): Sequential(
      (0): Linear(in_features=2048, out_features=256, bias=True)
      (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
    )
    (5): Sequential(
      (0): Linear(in_features=2048, out_features=256, bias=True)
      (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
    )
  )
  (att_fc_list): ModuleList(
    (0): Sequential(
      (0): Linear(in_features=1536, out_features=1536, bias=True)
      (1): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): Linear(in_features=1536, out_features=1536, bias=True)
      (4): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (5): Linear(in_features=1536, out_features=5, bias=True)
    )
    (1): Sequential(
      (0): Linear(in_features=1536, out_features=1536, bias=True)
      (1): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): Linear(in_features=1536, out_features=1536, bias=True)
      (4): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (5): Linear(in_features=1536, out_features=5, bias=True)
    )
    (2): Sequential(
      (0): Linear(in_features=1536, out_features=1536, bias=True)
      (1): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): Linear(in_features=1536, out_features=1536, bias=True)
      (4): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (5): Linear(in_features=1536, out_features=11, bias=True)
    )
    (3): Sequential(
      (0): Linear(in_features=1536, out_features=1536, bias=True)
      (1): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): Linear(in_features=1536, out_features=1536, bias=True)
      (4): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (5): Linear(in_features=1536, out_features=3, bias=True)
    )
    (4): Sequential(
      (0): Linear(in_features=1536, out_features=1536, bias=True)
      (1): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): Linear(in_features=1536, out_features=1536, bias=True)
      (4): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (5): Linear(in_features=1536, out_features=3, bias=True)
    )
    (5): Sequential(
      (0): Linear(in_features=1536, out_features=1536, bias=True)
      (1): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): Linear(in_features=1536, out_features=1536, bias=True)
      (4): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (5): Linear(in_features=1536, out_features=4, bias=True)
    )
    (6): Sequential(
      (0): Linear(in_features=1536, out_features=1536, bias=True)
      (1): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): Linear(in_features=1536, out_features=1536, bias=True)
      (4): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (5): Linear(in_features=1536, out_features=4, bias=True)
    )
    (7): Sequential(
      (0): Linear(in_features=1536, out_features=1536, bias=True)
      (1): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): Linear(in_features=1536, out_features=1536, bias=True)
      (4): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (5): Linear(in_features=1536, out_features=3, bias=True)
    )
    (8): Sequential(
      (0): Linear(in_features=1536, out_features=1536, bias=True)
      (1): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): Linear(in_features=1536, out_features=1536, bias=True)
      (4): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (5): Linear(in_features=1536, out_features=11, bias=True)
    )
    (9): Sequential(
      (0): Linear(in_features=1536, out_features=1536, bias=True)
      (1): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): Linear(in_features=1536, out_features=1536, bias=True)
      (4): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (5): Linear(in_features=1536, out_features=3, bias=True)
    )
  )
)

Extract Feature: 100%|#########################################################| 2000/2000 [01:01<00:00, 32.19 images/s]
/opt/anaconda/envs/torch_27_/lib/python2.7/site-packages/ipykernel/__main__.py:1: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
  if __name__ == '__main__':
/opt/anaconda/envs/torch_27_/lib/python2.7/site-packages/ipykernel/__main__.py:1: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
  if __name__ == '__main__':
/opt/anaconda/envs/torch_27_/lib/python2.7/site-packages/ipykernel/__main__.py:1: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
  if __name__ == '__main__':
/opt/anaconda/envs/torch_27_/lib/python2.7/site-packages/ipykernel/__main__.py:1: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
  if __name__ == '__main__':
/opt/anaconda/envs/torch_27_/lib/python2.7/site-packages/ipykernel/__main__.py:1: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
  if __name__ == '__main__':
/opt/anaconda/envs/torch_27_/lib/python2.7/site-packages/ipykernel/__main__.py:1: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
  if __name__ == '__main__':
/opt/anaconda/envs/torch_27_/lib/python2.7/site-packages/ipykernel/__main__.py:1: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
  if __name__ == '__main__':
/opt/anaconda/envs/torch_27_/lib/python2.7/site-packages/ipykernel/__main__.py:1: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
  if __name__ == '__main__':
/opt/anaconda/envs/torch_27_/lib/python2.7/site-packages/ipykernel/__main__.py:1: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
  if __name__ == '__main__':
/opt/anaconda/envs/torch_27_/lib/python2.7/site-packages/ipykernel/__main__.py:1: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
  if __name__ == '__main__':

年龄类型
背包类型
上衣颜色
上衣类型
发型
帽子类型
种族类型
性别
裤子颜色
裤子类型
年龄类型
背包类型
上衣颜色
上衣类型
发型
帽子类型
种族类型
性别
裤子颜色
裤子类型
count     2000
unique       2
top       True
freq      1974
Name: 年龄类型, dtype: object
count     2000
unique       2
top       True
freq      1970
Name: 背包类型, dtype: object
count     2000
unique       2
top       True
freq      1976
Name: 上衣颜色, dtype: object
count     2000
unique       2
top       True
freq      1983
Name: 上衣类型, dtype: object
count     2000
unique       2
top       True
freq      1976
Name: 发型, dtype: object
count     2000
unique       2
top       True
freq      1994
Name: 帽子类型, dtype: object
count     2000
unique       2
top       True
freq      1999
Name: 种族类型, dtype: object
count     2000
unique       2
top       True
freq      1985
Name: 性别, dtype: object
count     2000
unique       2
top       True
freq      1980
Name: 裤子颜色, dtype: object
count     2000
unique       2
top       True
freq      1988
Name: 裤子类型, dtype: object

前向计算Â¶

model_feat = ftnet.infer_im_list(label.index, get_pap_mask=None)

attr_list, feat, im_path = model_feat.values()
feat, im_path

([tensor([[ -5.6249,   4.1532,   8.3039,  -1.6738,  -5.1619],
          [ -9.9699,  12.2879,   7.6833,  -6.8287,  -3.1700],
          [ -5.8325,   5.2775,   0.0324,   3.2872,  -2.7674],
          ...,
          [ -4.1013,   6.3318,   2.0590,  -2.5140,  -1.7774],
          [ -2.8577,   3.4234,   5.8742,  -6.2104,  -0.2283],
          [-11.8013,  11.0921,   3.3092,  -0.3036,  -2.3047]], device='cuda:0'),
  tensor([[ 4.6952,  2.6957, -1.5069, -3.5335, -2.3508],
          [ 9.1722,  1.9383, -2.7194, -3.0980, -5.2956],
          [ 3.2863,  0.3184,  3.6919, -2.6898, -4.6044],
          ...,
          [ 8.1219, -1.2354, -2.3800, -4.2145, -0.2898],
          [ 6.7575,  0.7425, -2.8345, -1.8857, -2.7774],
          [ 1.7837, -3.5186,  0.9801,  8.6391, -7.8807]], device='cuda:0'),
  tensor([[  2.4536,   3.9094,   3.0124,  ...,  -1.4861,  -1.0059, -10.4474],
          [ 13.5689,  -2.2533,   1.5948,  ...,   1.2007,  -6.9916,  -4.6730],
          [ 14.8001,   0.7079,   2.8141,  ...,   2.7863,  -7.7448,  -2.8902],
          ...,
          [ -2.2120,   2.6939,   9.7328,  ...,  -1.8162,   1.3563,  -0.9288],
          [  0.8157,   6.9568,  -0.2328,  ...,   0.2882,  -4.5459,  -3.0529],
          [  2.9420,  14.2281,  -0.4574,  ...,   1.7902,  -6.2215,  -6.7022]],
         device='cuda:0'),
  tensor([[-0.5384,  7.3543, -6.8209],
          [-2.9909, 10.8880, -7.9046],
          [ 1.1913,  5.8952, -7.0889],
          ...,
          [ 5.1092, -1.1282, -3.9773],
          [ 4.7295,  1.8513, -6.5798],
          [-0.4206,  8.1170, -7.6936]], device='cuda:0'),
  tensor([[ 1.2128,  7.4656, -8.6815],
          [-1.7111,  7.8013, -6.0860],
          [ 8.2149,  0.0559, -8.2673],
          ...,
          [-5.7828, -0.4156,  6.1957],
          [-0.4606,  3.0922, -2.6391],
          [ 6.1239,  0.9370, -7.0587]], device='cuda:0'),
  tensor([[ 10.1167,   4.5911,  -4.3366, -10.3953],
          [  7.8497,  -0.3247,  -3.1720,  -4.3712],
          [  8.5630,  -2.9950,  -1.0933,  -4.4886],
          ...,
          [  0.6702,  -1.9773,   8.1789,  -6.8757],
          [  6.3722,  -4.4936,   1.6056,  -3.4998],
          [  7.9690,   0.0687,  -6.2317,  -1.8147]], device='cuda:0'),
  tensor([[14.0922, -5.8259, -2.2178, -6.0799],
          [11.3591, -1.4486, -2.4286, -7.4943],
          [11.2057, -5.4718, -2.6001, -3.1483],
          ...,
          [11.7820, -5.5807, -2.3727, -3.8521],
          [10.0755, -5.9360, -2.4329, -1.7271],
          [10.6348, -5.1726, -2.1922, -3.2953]], device='cuda:0'),
  tensor([[ 6.9920e+00, -7.4656e-02, -6.9184e+00],
          [ 8.6479e+00, -1.7721e+00, -6.8789e+00],
          [ 2.6468e+00,  2.5073e-03, -2.6504e+00],
          ...,
          [ 5.5227e+00,  1.5692e+00, -7.0943e+00],
          [ 4.4354e+00, -3.2741e-01, -4.1102e+00],
          [-7.1203e-01,  6.4288e+00, -5.7165e+00]], device='cuda:0'),
  tensor([[ 3.4642,  6.5301,  0.1984,  ..., -3.2585, -1.8088,  6.9953],
          [ 1.2739, 14.8428, -4.7658,  ...,  5.3987, -3.1048,  3.5018],
          [13.2272,  2.8891, -3.7298,  ...,  5.3598, -0.7360, -3.3749],
          ...,
          [-5.0075,  3.6994, -4.3981,  ...,  0.1424, -2.3924,  8.7202],
          [-0.0363,  7.2754, -0.0934,  ..., -1.2016, -4.3049, -0.8986],
          [-1.1819,  7.3831, -4.6997,  ...,  5.3567, -5.9785, -0.8324]],
         device='cuda:0'),
  tensor([[ 0.9030, -1.9599,  1.0580],
          [ 7.0896, -2.3413, -4.7476],
          [ 7.5951,  0.7861, -8.3855],
          ...,
          [ 1.0857, -6.6552,  5.5716],
          [ 6.5062,  1.2332, -7.7418],
          [ 4.8221, -1.2856, -3.5461]], device='cuda:0')],
 ['/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073908_06.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073922_02.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073938_04.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073962_05.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073965_08.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073967_13.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073972_03.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073976_03.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073978_01.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073978_08.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073983_15.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073992_12.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074004_09.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074022_07.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074030_04.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074059_14.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074063_02.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074079_05.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074081_02.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074110_00.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074135_02.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074191_07.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074225_04.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074239_08.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074247_01.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074252_08.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074259_03.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074260_01.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074271_10.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074304_01.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074305_03.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074318_01.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074333_00.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074386_00.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074388_01.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074390_10.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074398_02.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074408_13.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074421_13.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074438_01.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074501_01.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074512_00.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074516_06.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074539_05.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074545_02.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074565_03.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074569_04.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074577_04.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074582_06.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074590_08.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074591_02.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074621_07.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074633_02.jpg',
  '/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074721_02.jpg',
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  ...])

att_prob = pd.DataFrame(map (lambda x : F.softmax(x).cpu().numpy().tolist(), model_feat['attr_list']), 
                        columns=im_path, index=attr_index).T
att_prob

比较label和att_probÂ¶

att_prob = att_prob.applymap(lambda x:x.index(max(x))).sort_index(axis=0)
att_prob

label

get_gt(att_prob, label)

计算mAPÂ¶

summery = get_gt(att_prob, label).describe()
summery

计算各个属性的平均准确率Â¶

_AP = []
for col in summery.columns:
    temp = summery[col]
    _AP.append(float(temp['freq']) / (temp['count']))
 
_AP = pd.Series(_AP, index=attr_index, name='_AP')
_AP

年龄类型    0.9870
背包类型    0.9850
上衣颜色    0.9880
上衣类型    0.9915
发型      0.9880
帽子类型    0.9970
种族类型    0.9995
性别      0.9925
裤子颜色    0.9900
裤子类型    0.9940
Name: _AP, dtype: float64

计算所有属性的平均准确率Â¶

mAP = _AP.mean()
mAP

0.99125

查看图片效果Â¶

for path, annotation in att_prob.T.items():
    
    # 图片路径,对应标注
    display( Image( filename =  path ), end='\t')
    display(annotation)

年龄类型     2
背包类型     0
上衣颜色     4
上衣类型     1
发型       1
帽子类型     0
种族类型     0
性别       0
裤子颜色    10
裤子类型     2
Name: /run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073908_06.jpg, dtype: int64

年龄类型    1
背包类型    0
上衣颜色    0
上衣类型    1
发型      1
帽子类型    0
种族类型    0
性别      0
裤子颜色    1
裤子类型    0
Name: /run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073922_02.jpg, dtype: int64

年龄类型    1
背包类型    2
上衣颜色    0
上衣类型    1
发型      0
帽子类型    0
种族类型    0
性别      0
裤子颜色    0
裤子类型    0
Name: /run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073938_04.jpg, dtype: int64

年龄类型     1
背包类型     0
上衣颜色     0
上衣类型     1
发型       0
帽子类型     0
种族类型     0
性别       1
裤子颜色    10
裤子类型     2
Name: /run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073962_05.jpg, dtype: int64

年龄类型    1
背包类型    3
上衣颜色    8
上衣类型    1
发型      0
帽子类型    0
种族类型    0
性别      1
裤子颜色    1
裤子类型    1
Name: /run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073965_08.jpg, dtype: int64

年龄类型    1
背包类型    0
上衣颜色    8
上衣类型    1
发型      0
帽子类型    0
种族类型    0
性别      1
裤子颜色    1
裤子类型    0
Name: /run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073967_13.jpg, dtype: int64

年龄类型    1
背包类型    0
上衣颜色    1
上衣类型    1
发型      1
帽子类型    0
种族类型    0
性别      0
裤子颜色    1
裤子类型    0
Name: /run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073972_03.jpg, dtype: int64

年龄类型    1
背包类型    0
上衣颜色    0
上衣类型    1
发型      1
帽子类型    0
种族类型    0
性别      0
裤子颜色    8
裤子类型    1
Name: /run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073976_03.jpg, dtype: int64

年龄类型    2
背包类型    1
上衣颜色    0
上衣类型    1
发型      1
帽子类型    0
种族类型    0
性别      0
裤子颜色    8
裤子类型    0
Name: /run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073978_01.jpg, dtype: int64

年龄类型    1
背包类型    0
上衣颜色    4
上衣类型    1
发型      0
帽子类型    0
种族类型    0
性别      1
裤子颜色    1
裤子类型    1
Name: /run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073978_08.jpg, dtype: int64

	年龄类型	背包类型	上衣颜色	上衣类型	发型	帽子类型	种族类型	性别	裤子颜色	裤子类型
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	年龄类型	背包类型	上衣颜色	上衣类型	发型	帽子类型	种族类型	性别	裤子颜色	裤子类型
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073908_06.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073922_02.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073938_04.jpg	False	False	True	True	True	True	True	False	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073962_05.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073965_08.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073967_13.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073972_03.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073976_03.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073978_01.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073978_08.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073983_15.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1073992_12.jpg	True	True	True	True	True	True	True	True	False	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074004_09.jpg	True	True	False	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074022_07.jpg	True	True	True	True	True	True	True	True	True	False
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074030_04.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074059_14.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074063_02.jpg	True	True	True	True	True	True	True	False	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074079_05.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074081_02.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074110_00.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074135_02.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074191_07.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074225_04.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074239_08.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074247_01.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074252_08.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074259_03.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074260_01.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074271_10.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person/1074304_01.jpg	True	True	True	True	True	True	True	True	True	True
...	...	...	...	...	...	...	...	...	...	...
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1220381_000001462.jpg	False	True	True	True	False	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1220426_000001602.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1220616_000001670.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221079_000001844.jpg	False	True	False	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221096_000001889.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221135_000001992.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221150_000002015.jpg	True	True	False	True	True	True	True	True	False	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221180_000002142.jpg	True	True	False	True	True	True	True	True	False	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221195_000002210.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221218_000002338.jpg	True	True	True	False	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221247_000002493.jpg	True	True	True	True	True	True	True	True	True	True
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/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221260_000002589.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221294_000002652.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221307_000002719.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221365_000002950.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221371_000002991.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221414_000003159.jpg	True	True	False	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221439_000003236.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221442_000003242.jpg	True	True	True	True	False	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221451_000003261.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221461_000003316.jpg	True	False	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221464_000003329.jpg	True	True	True	False	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221487_000003445.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221506_000003596.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221509_000003629.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221534_000003724.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221612_000004058.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221651_000004188.jpg	True	True	True	True	True	True	True	True	True	True
/run/media/geoffrey/Timbersaw/DataSet/dataset/Coop/person5/person/1221673_000004289.jpg	True	True	True	True	True	True	True	True	True	True

	年龄类型	背包类型	上衣颜色	上衣类型	发型	帽子类型	种族类型	性别	裤子颜色	裤子类型
count	2000	2000	2000	2000	2000	2000	2000	2000	2000	2000
unique	2	2	2	2	2	2	2	2	2	2
top	True	True	True	True	True	True	True	True	True	True
freq	1974	1970	1976	1983	1976	1994	1999	1985	1980	1988